Import avocado data.

## # A tibble: 10 x 15
##    date        year month   day average_price total_volume small  large
##    <date>     <int> <int> <int>         <dbl>        <dbl> <dbl>  <dbl>
##  1 2015-12-27  2015    12    27          1.33       64237. 1037. 5.45e4
##  2 2015-12-20  2015    12    20          1.35       54877.  674. 4.46e4
##  3 2015-12-13  2015    12    13          0.93      118220.  795. 1.09e5
##  4 2015-12-06  2015    12     6          1.08       78992. 1132  7.20e4
##  5 2015-11-29  2015    11    29          1.28       51040.  941. 4.38e4
##  6 2015-11-22  2015    11    22          1.26       55980. 1184. 4.81e4
##  7 2015-11-15  2015    11    15          0.99       83454. 1369. 7.37e4
##  8 2015-11-08  2015    11     8          0.98      109428.  704. 1.02e5
##  9 2015-11-01  2015    11     1          1.02       99811. 1022. 8.73e4
## 10 2015-10-25  2015    10    25          1.07       74339.  842. 6.48e4
## # … with 7 more variables: extra_large <dbl>, total_bags <dbl>,
## #   small_bags <dbl>, large_bags <dbl>, x_large_bags <dbl>, type <chr>,
## #   region <chr>
## tibble [18,249 × 15] (S3: tbl_df/tbl/data.frame)
##  $ date         : Date[1:18249], format: "2015-12-27" "2015-12-20" ...
##  $ year         : int [1:18249] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
##  $ month        : int [1:18249] 12 12 12 12 11 11 11 11 11 10 ...
##  $ day          : int [1:18249] 27 20 13 6 29 22 15 8 1 25 ...
##  $ average_price: num [1:18249] 1.33 1.35 0.93 1.08 1.28 1.26 0.99 0.98 1.02 1.07 ...
##  $ total_volume : num [1:18249] 64237 54877 118220 78992 51040 ...
##  $ small        : num [1:18249] 1037 674 795 1132 941 ...
##  $ large        : num [1:18249] 54455 44639 109150 71976 43838 ...
##  $ extra_large  : num [1:18249] 48.2 58.3 130.5 72.6 75.8 ...
##  $ total_bags   : num [1:18249] 8697 9506 8145 5811 6184 ...
##  $ small_bags   : num [1:18249] 8604 9408 8042 5677 5986 ...
##  $ large_bags   : num [1:18249] 93.2 97.5 103.1 133.8 197.7 ...
##  $ x_large_bags : num [1:18249] 0 0 0 0 0 0 0 0 0 0 ...
##  $ type         : chr [1:18249] "conventional" "conventional" "conventional" "conventional" ...
##  $ region       : chr [1:18249] "Albany" "Albany" "Albany" "Albany" ...
## [1] 0

Description:
year: 2015-2018
month: 1-12
day: 1-31
type: conventional, organic
fruit_size: small, large, extra_large
bag_type: total, small, large, extra_large

## # A tibble: 10 x 3
##    area          year    gdp
##    <chr>         <chr> <dbl>
##  1 United States 2015  50301
##  2 United States 2016  50660
##  3 United States 2017  51337
##  4 Alabama       2015  36818
##  5 Alabama       2016  37158
##  6 Alabama       2017  37508
##  7 Alaska        2015  65971
##  8 Alaska        2016  63304
##  9 Alaska        2017  63610
## 10 Arizona       2015  38787
## [1] 0

Description:
year: 2015-2017

wo jue de ke yi zhao you guan xi de che yi che ? https://www.medicalnewstoday.com/articles/270406#benefits https://pdf.usaid.gov/pdf_docs/PA00KP28.pdf

yao bu zhe li zai gao dian data fao.org/faostat/en/#search/Avocados

https://quickstats.nass.usda.gov/results/8A9760E3-BDB0-3A88-B014-DA81BA0845BD

Volume consumption by year: conventional vs. organic

Time vs. Avocado Consumption by Region

Time vs. Avocado Price by Region

zhe ge tu you dian la ji … yan se ye gai bu liao bu zhi dao wei sha
Avocado Size vs. Volume Sold

Region vs. Year Average Volume Consumption